Developing Medical AI : a cloud-native audio-visual data collection study
Sagi Schein, Greg Arutiunian, Vitaly Burshtein, Gal Sadeh, Michelle, Townshend, Bruce Friedman, Shada Sadr-azodi

TL;DR
This paper presents a comprehensive approach to developing medical AI by designing a cloud-native system for audio-visual data collection, addressing the lack of publicly available datasets for patient deterioration detection.
Contribution
It introduces a novel protocol for audio-visual data collection, a cloud-based architecture for data processing, and a custom device for data acquisition in medical settings.
Findings
Established a new protocol for medical audio-visual data collection
Designed a scalable cloud architecture for data processing
Developed a specialized device for capturing patient data
Abstract
Designing Artificial Intelligence (AI) solutions that can operate in real-world situations is a highly complex task. Deploying such solutions in the medical domain is even more challenging. The promise of using AI to improve patient care and reduce cost has encouraged many companies to undertake such endeavours. For our team, the goal has been to improve early identification of deteriorating patients in the hospital. Identifying patient deterioration in lower acuity wards relies, to a large degree on the attention and intuition of clinicians, rather than on the presence of physiological monitoring devices. In these care areas, an automated tool which could continuously observe patients and notify the clinical staff of suspected deterioration, would be extremely valuable. In order to develop such an AI-enabled tool, a large collection of patient images and audio correlated with…
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Taxonomy
TopicsTime Series Analysis and Forecasting · Healthcare Technology and Patient Monitoring
